from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase          =						logging.get_logger(__name__)


if is_vision_available():
	import PIL





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   ['''pixel_values''']
				def __init__(		self						,      lowercase = True						,      lowercase = None						,      lowercase = PILImageResampling.BICUBIC						,      lowercase = True						,      lowercase = None						,      lowercase = True						,      lowercase = 1 / 2_5_5						,      lowercase = True						,      lowercase = None						,      lowercase = None						,      lowercase = True						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(**lowercase   )
						A_						:							Any      = size if size is not None else {'shortest_edge': 2_2_4}
						A_						:							Optional[int]      = get_size_dict(lowercase						,      default_to_square=lowercase   )
						A_						:							Dict      = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_						:							Union[str, Any]      = get_size_dict(lowercase						,      default_to_square=lowercase						,      param_name='crop_size'   )

						A_						:							str      = do_resize
						A_						:							List[str]      = size
						A_						:							int      = resample
						A_						:							Dict      = do_center_crop
						A_						:							Dict      = crop_size
						A_						:							List[Any]      = do_rescale
						A_						:							int      = rescale_factor
						A_						:							Tuple      = do_normalize
						A_						:							int      = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						A_						:							List[str]      = image_std if image_std is not None else OPENAI_CLIP_STD
						A_						:							Union[str, Any]      = do_convert_rgb
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase = PILImageResampling.BICUBIC						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = get_size_dict(lowercase						,      default_to_square=lowercase   )
						if "shortest_edge" not in size:
								raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
						A_						:							str      = get_resize_output_image_size(lowercase						,      size=size['shortest_edge']						,      default_to_square=lowercase   )
						return resize(lowercase						,      size=lowercase						,      resample=lowercase						,      data_format=lowercase						,      **lowercase   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							List[Any]      = get_size_dict(lowercase   )
						if "height" not in size or "width" not in size:
								raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''   )
						return center_crop(lowercase						,      size=(size['height'], size['width'])						,      data_format=lowercase						,      **lowercase   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						return rescale(lowercase						,      scale=lowercase						,      data_format=lowercase						,      **lowercase   )
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						return normalize(lowercase						,      mean=lowercase						,      std=lowercase						,      data_format=lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = None						,      lowercase = ChannelDimension.FIRST						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							Dict      = do_resize if do_resize is not None else self.do_resize
						A_						:							Optional[Any]      = size if size is not None else self.size
						A_						:							Optional[int]      = get_size_dict(lowercase						,      param_name='size'						,      default_to_square=lowercase   )
						A_						:							Optional[Any]      = resample if resample is not None else self.resample
						A_						:							Dict      = do_center_crop if do_center_crop is not None else self.do_center_crop
						A_						:							str      = crop_size if crop_size is not None else self.crop_size
						A_						:							Union[str, Any]      = get_size_dict(lowercase						,      param_name='crop_size'						,      default_to_square=lowercase   )
						A_						:							Union[str, Any]      = do_rescale if do_rescale is not None else self.do_rescale
						A_						:							Optional[Any]      = rescale_factor if rescale_factor is not None else self.rescale_factor
						A_						:							int      = do_normalize if do_normalize is not None else self.do_normalize
						A_						:							Optional[int]      = image_mean if image_mean is not None else self.image_mean
						A_						:							int      = image_std if image_std is not None else self.image_std
						A_						:							int      = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						A_						:							Dict      = make_list_of_images(lowercase   )

						if not valid_images(lowercase   ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

						if do_resize and size is None:
								raise ValueError('Size must be specified if do_resize is True.'   )

						if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.'   )

						if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

						if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
								A_						:							Dict      = [convert_to_rgb(lowercase   ) for image in images]

						# All transformations expect numpy arrays.
						A_						:							int      = [to_numpy_array(lowercase   ) for image in images]

						if do_resize:
								A_						:							Dict      = [self.resize(image=lowercase						,      size=lowercase						,      resample=lowercase   ) for image in images]

						if do_center_crop:
								A_						:							List[str]      = [self.center_crop(image=lowercase						,      size=lowercase   ) for image in images]

						if do_rescale:
								A_						:							List[Any]      = [self.rescale(image=lowercase						,      scale=lowercase   ) for image in images]

						if do_normalize:
								A_						:							List[str]      = [self.normalize(image=lowercase						,      mean=lowercase						,      std=lowercase   ) for image in images]

						A_						:							Dict      = [to_channel_dimension_format(lowercase						,      lowercase   ) for image in images]

						A_						:							Any      = {'pixel_values': images}
						return BatchFeature(data=lowercase						,      tensor_type=lowercase   )







def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					List[Any]		,__lowercase		:					Tuple      ):
		'''simple docstring'''




		if n == 0:
				return 1

		elif n % 2 == 1:
				return (binary_exponentiation(__lowercase		,n - 1		,__lowercase      ) * a) % mod

		else:
				A_						:							str      = binary_exponentiation(__lowercase		,n / 2		,__lowercase      )
				return (b * b) % mod


# a prime number
_UpperCAmelCase          =						701

_UpperCAmelCase          =						1000000000
_UpperCAmelCase          =						10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)


print((a / b) % p == (a * b ** (p - 2)) % p)






